Sherlock Holmes and the deductive paradigm of forensic epidemiology.

Earlier this year, Dom Mellor was giving a talk to the epidemiology group at Glasgow, where he started by saying that, in his view, Sherlock Holmes represented the perfect example of forensic epidemiology. In a sense he was right, and at least some of you will know that it is commonly believed that the Holmesian forensic technique was based on Conan Doyle’s experiences as an Edinburgh medical student, where the medical doctor and University Professor Joseph Bell impressed the young student in his lectures. It was said that “all Edinburgh medical students remember Joseph Bell – Joe Bell  as they called him. Always alert, always up and doing, nothing ever escaped that keen eye of his. He read both patients and students like so many open books. His diagnosis was almost never at fault.” Sherlock Holmes most famous quote, taken from the Sign of the Four: “When you have eliminated the impossible, whatever remains, however improbable, must be the truth” is the iconic expression of deductive logic, and it could be said to be the ultimate goal of forensic epidemiology. I recall a colleague saying to me “Identify and eliminate the source of Infection, and you eliminate the epidemic”, apparently quoting from the highly respected veterinary epidemiologist, Prof. Mike Thrusfield at the Dick Vet School in Edinburgh, though I cannot comment on the accuracy of the quote. Of course, it is also well recognised that it would usually be impossible to be so sure as Sherlock Holmes in real life, but this nevertheless represents a sort of platonic ideal of forensics.

Move forward a century and more, and the hugely popular TV series ‘Sherlock’ presents a modern updating of the old stories, an updating which, to my great surprise, I have thoroughly enjoyed. In the third series, in the episode ‘Hearse, Sign and Vow’, Sherlock and his older, more intelligent brother Mycroft are engaged in a contest to characterise a man from only his woolly hat. In this contest Sherlock queries one of Mycroft’s “deductions”, when Mycroft replies “Balance of probabilities, little brother.” Now this statement is decidedly un-Holmesian – in the world of Arthur Conan Doyle’s Sherlock Holmes, probabilities have nothing to do with it. This statement is in fact, one of inductive logic. And it could be argued that the mathematical and statistical modelling of infectious diseases lies very much more in this inductive tradition. Not so much concerned with identifying the single chain of transmission, modelling traditionally concentrates on the identification of general, population level principles of transmission, and an overall ‘balance of probabilities’ of getting the right pattern.

These two traditions – that of the forensic epidemiologist and the mathematical/statistical epidemiologist do not sit easily together, and indeed it could be argued that much of the controversy over the 2001 Foot-and-mouth disease (FMD) epidemic in Great Britain can be attributed to precisely that clash of cultures.

Phylodynamic reconstruction of a cluster of cases from the 2001 FMD epidemic in Great Britain. (A) Identified likelihood that a particular infected premises was the source of another infected premises based on a space–time–genetic model. Circle size is proportional to the relative likelihood of that event. (B) Spatial relationships among premises in the dataset. Adapted from Morelli et al. PLoS Pathogens 2012.

Now however, the integration of rapid high throughout sequencing of pathogens allows us to trace to a very fine scale the movement of pathogens from place-to-place, and even from individual-to-individual. Combined with mathematical models, this can often lead to very precise identification of likely sources of infection. The figure here is taken from a paper by Marco Morelli while he was working with Dan Haydon at Glasgow, illustrating precisely that kind of analysis using data from the 2001 FMD epidemic. Of course the most likely source under one model of transmission is not necessarily proof that the relationship is the true one (e.g. what if another model gives an equally strong but different prediction?) and there are many challenges still to be addressed. Despite these issues, the future is bright and it is just possible that, through these new technologies and approaches, we can at last approach that Holmesian ideal.

2 comments

By seeing patterns beyond the blatant clustering of social groups Don Francis’ deductions in the early days of HIV may represent an example of sitting somewhere in the middle.
Never realised Sherlock would be your thing Rowland!

An interesting example – and one I didn’t really know except in the broadest sense, until I googled it just now – thanks for that.

I think there are probably more examples and it must be true that, in the end, good scientists/epidemiologists from either (or for that matter any) perspective will see beyond the caricaturised limitations I’ve suggested. Nevertheless I do think that the points of view are relevant, particularly under situations when individuals are put under pressure we can very much revert to ‘what we know’, and I think that was a factor in 2001.